294 citations · 1.1k across the 67 of their papers we have counts for
28 papers · 1 filter
A Survey on Protein Representation Learning: Retrospect and Prospect
Lirong Wu, Yufei Huang, Haitao Lin +1
Proteins are fundamental biological entities that play a key role in life activities. The amino acid sequences of proteins can be folded into stable 3D structures in the real physi…
Federated Learning for Inference at Anytime and Anywhere
Zicheng Liu, Da Li, Javier Fernandez-Marques +6
Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication…
Protein Language Models and Structure Prediction: Connection and Progression
Bozhen Hu, Jun Xia, Jiangbin Zheng +4
The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding. Recent advances have…
EVNet: An Explainable Deep Network for Dimension Reduction
Zelin Zang, Shenghui Cheng, Linyan Lu +7
Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful propertie…
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier
Zelin Zang, Lei Shang, Senqiao Yang +4
Unsupervised domain adaptation (UDA) has proven to be highly effective in transferring knowledge from a label-rich source domain to a label-scarce target domain. However, the prese…
DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
Haitao Lin, Yufei Huang, Odin Zhang +7
Generating molecules that bind to specific proteins is an important but challenging task in drug discovery. Previous works usually generate atoms in an auto-regressive way, where e…
MogaNet: Multi-order Gated Aggregation Network
Siyuan Li, Zedong Wang, Zicheng Liu +6
By contextualizing the kernel as global as possible, Modern ConvNets have shown great potential in computer vision tasks. However, recent progress on multi-order game-theoretic int…
Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translation
Jiangbin Zheng, Siyuan Li, Cheng Tan +3
Sign Language (SL), as the mother tongue of the deaf community, is a special visual language that most hearing people cannot understand. In recent years, neural Sign Language Trans…
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation
Lirong Wu, Yufei Huang, Haitao Lin +3
Self-supervised learning on graphs has recently achieved remarkable success in graph representation learning. With hundreds of self-supervised pretext tasks proposed over the past…
Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification
Lirong Wu, Jun Xia, Haitao Lin +4
Recent years have witnessed great success in handling graph-related tasks with Graph Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons (MLPs) re…
PiFold: Toward effective and efficient protein inverse folding
Zhangyang Gao, Cheng Tan, Pablo Chacón +1
How can we design protein sequences folding into the desired structures effectively and efficiently? AI methods for structure-based protein design have attracted increasing attenti…
Metaball-Imaging Discrete Element Lattice Boltzmann Method for fluid-particle system of complex morphologies with settling case study
Yifeng Zhao, Pei Zhang, Liang Lei +2
Fluid-particle systems are highly sensitive to particle morphologies. While many attempts have been made on shape descriptors and coupling schemes, how to simulate the particle-par…
Exploring Generative Neural Temporal Point Process
Haitao Lin, Lirong Wu, Guojiang Zhao +2
Temporal point process (TPP) is commonly used to model the asynchronous event sequence featuring occurrence timestamps and revealed by probabilistic models conditioned on historica…
What Does the Gradient Tell When Attacking the Graph Structure
Zihan Liu, Ge Wang, Yun Luo +1
Recent research has revealed that Graph Neural Networks (GNNs) are susceptible to adversarial attacks targeting the graph structure. A malicious attacker can manipulate a limited n…
Exploiting Sentiment and Common Sense for Zero-shot Stance Detection
Yun Luo, Zihan Liu, Yuefeng Shi +2
The stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot set…
DLME: Deep Local-flatness Manifold Embedding
Zelin Zang, Siyuan Li, Di Wu +5
Manifold learning (ML) aims to seek low-dimensional embedding from high-dimensional data. The problem is challenging on real-world datasets, especially with under-sampling data, an…
CoSP: Co-supervised pretraining of pocket and ligand
Zhangyang Gao, Cheng Tan, Lirong Wu +1
Can we inject the pocket-ligand interaction knowledge into the pre-trained model and jointly learn their chemical space? Pretraining molecules and proteins has attracted considerab…
SimVP: Simpler yet Better Video Prediction
Zhangyang Gao, Cheng Tan, Lirong Wu +1
From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training str…
Hyperspherical Consistency Regularization
Cheng Tan, Zhangyang Gao, Lirong Wu +2
Recent advances in contrastive learning have enlightened diverse applications across various semi-supervised fields. Jointly training supervised learning and unsupervised learning…
STONet: A Neural-Operator-Driven Spatio-temporal Network
Haitao Lin, Guojiang Zhao, Lirong Wu +1
Graph-based spatio-temporal neural networks are effective to model the spatial dependency among discrete points sampled irregularly from unstructured grids, thanks to the great exp…
Generative De Novo Protein Design with Global Context
Cheng Tan, Zhangyang Gao, Jun Xia +2
The linear sequence of amino acids determines protein structure and function. Protein design, known as the inverse of protein structure prediction, aims to obtain a novel protein s…
DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations
Fang Wu, Stan Z. Li
Molecular dynamics (MD) has long been the de facto choice for simulating complex atomistic systems from first principles. Recently deep learning models become a popular way to acce…
Pre-training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding
Fang Wu, Shuting Jin, Yinghui Jiang +7
The latest biological findings observe that the traditional motionless 'lock-and-key' theory is not generally applicable because the receptor and ligand are constantly moving. None…
A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications
Jun Xia, Yanqiao Zhu, Yuanqi Du +1
Pretrained Language Models (PLMs) such as BERT have revolutionized the landscape of Natural Language Processing (NLP). Inspired by their proliferation, tremendous efforts have been…